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Separable Attention Capsule Network for Signal Classification
- Source :
- IEEE Access, Vol 8, Pp 181744-181750 (2020)
- Publication Year :
- 2020
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2020.
-
Abstract
- In this paper, a new Separable Attention Capsule Network (SACN) is proposed for signal classification. SACN is a light-weight network composed of multi-channel separable convolution layer, attention module and classification layer. First, depth-wise convolution is employed to extract features of signals in a low-complexity manner, and the multi-channel network structure is designed to increase the network width to improve the diversity of features of signals. Then a channel attention module is followed by a capsule network whose element contains a group of neurons. This attention module can explore the interdependence among channels to use global information to selectively strengthen some important channels, thus achieving the improvement of generalization ability of SACN. Some experiments are taken on several datasets with communication and radar signals, and the comparison results prove the efficiency of SACN and the superiority to its counterparts.
- Subjects :
- General Computer Science
Computer science
Feature extraction
02 engineering and technology
law.invention
Separable space
Separable convolution
Signal classification
law
0202 electrical engineering, electronic engineering, information engineering
General Materials Science
Radar
channel attention
business.industry
General Engineering
capsule network
020206 networking & telecommunications
Pattern recognition
multi-channel
Kernel (image processing)
020201 artificial intelligence & image processing
lcsh:Electrical engineering. Electronics. Nuclear engineering
Artificial intelligence
business
lcsh:TK1-9971
Communication channel
Subjects
Details
- ISSN :
- 21693536
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....bc9200c858458f891366fde3f88407ed